Selecting a Proper Neuromorphic Platform for the Intelligent IoT

Abstract

With the rapid development of the Internet of Things (IoT) and AI technology, IoT services based on Artificial Intelligence (AI) technology are becoming more and more intelligent. To provide these intelligent IoT services, IoT hardware and IoT software must support AI technology. In general, battery-powered IoT devices have limited computing power compared to general-purpose computers. Therefore, to implement various intelligent IoT services, it must be able to support AI technology with low power to IoT devices. The low-power Neuromorphic architecture can enable resource-limited IoT devices to provide intelligent IoT services based on AI technology. In this paper, we propose a Neuromorphic Architecture Abstraction (NAA) model for providing an efficient intelligent IoT service. The proposed NAA model dynamically selects the proper Neuromorphic architecture according to the characteristics of the training target architecture and increases the training speed and training success rate. We also implement the proposed model in a real IoT computing environment and show that the proposed NAA model can reduce the training speed and reduce the training models success rate compared with the method of randomly specifying the Neuromorphic architecture.